Controlling and monitoring large-scale industrial assets (e.g., chemical plants, fuel cells, power turbines, nuclear power plants, etc) often generates a huge amount of data. This data characterizes the complex dynamic behaviour of each asset (or component).
Commonly, there are two approaches in model-based diagnosis. The fist attempts to model the faulty behavior of a process. This approach tries to recognize the fault patterns from the available data. However, faulty conditions are rare and it takes a long time to build enough fault patterns to be efficient. The same fault can execute differently and may be not recognized by this method.
Another approach is to model normal behavior and to signal any deviation from this model. Data from normal operation is much easier to get for a process. The drawback of this approach lies in the difficulty in providing a good model that covers the different operating modes that are present during normal execution of a complex industrial process.
Improvements are therefore desired in the control and monitoring of large-scale industrial processes.